Energy management method for series hybrid electric vehicle

Abstract

The present invention discloses an energy management method for a series hybrid electric vehicle. During the driving process, the adjustment coefficient and the power distribution coefficient are obtained by the vehicle demand power, the battery SOC value and the battery temperature; the adjustment coefficient and the standard adjustment power are multiplied to obtain the adjustment power, the adjustment power and the engine power at the last moment are added to obtain the engine output power, and the engine speed is determined according to the engine output power and the engine minimum fuel consumption characteristic curve; the power distribution coefficient and the engine output power are multiplied to obtain the power output of the engine to the power battery, and the difference is the power emitted by the engine for driving the whole vehicle. During the braking process, when the battery temperature is less than the temperature threshold and the battery SOC value is less than the SOC threshold, the braking energy recovery method is used for braking. The present invention can prevent the battery from continuing to charge and discharge when the temperature is too high, thereby improving the service life of the power battery of the hybrid electric vehicle.

Description

Technical Field

[0001] The invention belongs to the field of energy management of hybrid electric vehicles, and in particular relates to an energy management method for a series hybrid electric vehicle. Background Art

[0002] Series hybrid electric vehicles combine the advantages of traditional internal combustion engine vehicles and electric vehicles, reducing fuel consumption and exhaust emissions while ensuring vehicle power performance. The energy management strategy of series hybrid electric vehicles can achieve the rational use of multiple power sources, and a reasonable energy management strategy can make each working condition in a state of high efficiency. Therefore, energy management strategy is a key step in the design of series hybrid electric vehicles. Fuzzy logic algorithms have been widely used in the energy management strategy of series hybrid electric vehicles because they do not require precise mathematical models, have strong robustness and good adaptability.

[0003] The traditional energy management strategy under the control of fuzzy logic algorithm only considers the battery SOC value and the vehicle power demand as input to determine the power demand of the series hybrid vehicle engine. When the battery SOC value is too low, the engine will charge the power battery through the generator, but if the battery temperature is too high at this time, the electrochemical reaction of the battery will be accelerated, the electrolyte will evaporate quickly, and the plate will be easily damaged. Continuing to charge will seriously shorten the battery life. In addition, the series hybrid vehicle will perform brake energy recovery during braking and store this energy in the power battery in the form of electrical energy, but braking energy recovery when the battery temperature is too high will also shorten the battery life. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides an energy management method for a series hybrid electric vehicle, which can prevent the battery from continuing to charge and discharge when the temperature is too high, thereby increasing the service life of the power battery of the hybrid electric vehicle.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An energy management method for a series hybrid electric vehicle, during the driving process of the hybrid electric vehicle, comprises the following steps:

[0007] S1, obtaining a regulation coefficient and a power allocation coefficient according to the vehicle power requirement, battery SOC value and battery temperature of the hybrid vehicle;

[0008] S2, multiplying the adjustment coefficient and the standard adjustment power to obtain the adjustment power, adding the adjustment power and the power of the hybrid vehicle engine at the previous moment to obtain the output power of the hybrid vehicle engine, and determining the engine speed of the hybrid vehicle according to the output power of the hybrid vehicle engine and the minimum fuel consumption characteristic curve of the hybrid vehicle engine;

[0009] S3, the power distribution coefficient and the output power P of the hybrid vehicle engine are compared. engine_i The power P output by the hybrid vehicle engine to the power battery is obtained by multiplying b , P engine_i With P b The difference is the power generated by the engine of the hybrid vehicle for driving the entire vehicle;

[0010] During the hybrid electric vehicle braking process, when the battery temperature is less than a temperature threshold and the battery SOC value is less than an SOC threshold, braking is performed using a braking energy recovery method.

[0011] Preferably, the vehicle power requirement of the hybrid vehicle in S1 is calculated according to the following formula:

[0012]

[0013] Where P req is the vehicle power requirement, v is the vehicle speed, m is the vehicle curb weight, g is the acceleration of gravity, f is the rolling resistance coefficient, α is the road slope, C D is the air resistance coefficient, A is the frontal area of ​​the vehicle, δ is the mass conversion coefficient, η t is the mechanical efficiency of the drive shaft.

[0014] Preferably, the standard regulation power described in S2 is within a range of 70% to 90% of the peak power of the hybrid vehicle engine.

[0015] Preferably, S1 first fuzzifies the vehicle demand power, battery SOC value and battery temperature of the hybrid vehicle into fuzzy vectors, then performs fuzzy reasoning to solve the fuzzy control rules corresponding to the fuzzy reasoning to obtain the fuzzy control quantities of the adjustment coefficient and the power allocation coefficient, and finally defuzzifies the fuzzy control quantities of the adjustment coefficient and the power allocation coefficient by combining the center of gravity method to obtain the adjustment coefficient and the power allocation coefficient.

[0016] Furthermore, when the vehicle power requirement is fuzzified, the basic domain is [0, P max ], and the corresponding fuzzy language values ​​are extremely small, small, medium, large and extremely large;

[0017] When the battery SOC value is fuzzified, the basic domain is [5%, 95%], and the corresponding fuzzy language values ​​are low, medium and high respectively;

[0018] When the battery temperature is fuzzified, the basic domain is [0°C, 40°C], and the corresponding fuzzy language values ​​are low, medium and high respectively.

[0019] Furthermore, the fuzzy language values ​​corresponding to the basic domain of the adjustment coefficient are respectively negative large, negative medium, negative small, zero, positive small, positive medium and positive large, and the fuzzy language values ​​corresponding to the basic domain of the power allocation coefficient are respectively very small, small, medium, large and very large.

[0020] Furthermore, the fuzzy reasoning described in S1 is performed using the Mamdani algorithm, and then the IF-THEN rule is used to determine the vehicle demand power P req , Battery SOC value SOC i , battery temperature T i And the corresponding adjustment coefficient K 1 and power distribution coefficient K 2 90 fuzzy control rules are established in sequence.

[0021] Furthermore, Mamdani fuzzy reasoning method is used in S1 to obtain K 1 and K 2 The fuzzy control quantity is as follows:

[0022] S11, let T i , SOC i , P req The sets formed by the fuzzy subsets are set A, set B, set C, K 1 , K 2 The sets formed by the fuzzy subsets are set X and set Y.

[0023] According to Mamdani fuzzy reasoning method, we have:

[0024] R(K 1 )=μ A (x)Λμ B (y)Λμ C (z)Λμ X (K 1 )

[0025] R(K 2 )=μ A (x)Λμ B (y)Λμ C (z)Λμ Y (K 2 )

[0026] Where: R(K 1)、R(K 2 ) are the SOC input in fuzzy reasoning. i , T i , P req And the output K 1 , K 2 The fuzzy relationship between them, the operation corresponding to Λ is to take the smaller one, μ A (x), μ B (y), μ C (z), μ X (K 1 ), μ Y (K 2 ) are T i , SOC i , P req , K 1 and K 2 The membership function of

[0027] S12, according to R(K 1 ) to get K 1 The total output of fuzzy reasoning According to R(K 2 ) to get K 2 The total output of fuzzy reasoning

[0028] S13, and Using the center of gravity method, the fuzzy control quantities of the adjustment coefficient and the power allocation coefficient are defuzzified to obtain K 1 and K 2 .

[0029] Furthermore, S13 K 1 and K 2 According to the following formulas:

[0030]

[0031]

[0032] Where: x 1 is the value in the basic domain of the adjustment coefficient, x 2 is the value within the basic domain of the power allocation coefficient.

[0033] Preferably, during the braking process of the hybrid electric vehicle, a mechanical braking method is used for braking when one of the following two situations occurs: the first situation is that the battery temperature is greater than or equal to the temperature threshold, and the second situation is that the battery SOC value is greater than or equal to the SOC threshold.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention discloses an energy management method for a series hybrid electric vehicle. The method first obtains an adjustment coefficient and a power distribution coefficient by using a battery SOC value, a vehicle power requirement, and a battery temperature. Then, the engine output power P is obtained by using the adjustment coefficient, the standard adjustment power, and the engine power at the last moment. engine_i , P engine_i The engine speed can be determined in combination with the engine minimum fuel consumption characteristic curve, thereby achieving the purpose of controlling the series hybrid vehicle in the power source. engine_i Multiplying them together, we can get the power P output from the engine to the power battery. b , P engine_i With P b The difference is the power generated by the engine for driving the whole vehicle. In this way, the size and distribution of the engine power demand are determined, and the power battery is refused to be charged when the battery temperature is too high. When the car brakes, the set battery temperature is compared with the battery temperature threshold, and the battery SOC value is compared with the battery SOC threshold, and the logic threshold control method is used to determine whether to perform brake energy recovery, thereby ensuring the service life of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is the fuzzy logic control flow chart of the present invention;

[0037] Figure 2 It is a membership function diagram of the battery SOC value of the present invention;

[0038] Figure 3 It is a battery temperature membership function diagram of the present invention;

[0039] Figure 4 The membership function diagram of the vehicle demand power of the present invention;

[0040] Figure 5 It is the adjustment coefficient membership function diagram of the present invention;

[0041] Figure 6 It is a power allocation coefficient membership function diagram of the present invention;

[0042] Figure 7 This is a flow chart of the logic threshold control method of the present invention;

[0043] Figure 8 X of the present invention i * The corresponding fuzzy reasoning output diagram;

[0044] Fig. 9 This is the minimum fuel consumption characteristic curve of the engine described in the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0047] The present invention discloses an energy management method for a series hybrid electric vehicle. During the driving process of the vehicle, the output power of the engine is controlled and distributed. During the braking process of the vehicle, a logic threshold value control method is used to control whether to perform braking energy recovery.

[0048] First, see Figure 1 In the process of driving the car, the required power of the whole vehicle is calculated first, and the required power of the whole vehicle, the battery SOC value and the battery temperature are used as the input of the fuzzy control module, wherein the fuzzy control module of the present invention includes a fuzzification interface, a knowledge base, an inference engine and a defuzzification interface. The fuzzification interface fuzzifies the input value into a fuzzy vector. The knowledge base includes a database and a rule base. The database stores the membership functions of all input and output variables and is used to provide data to the inference engine. The rule base stores fuzzy rules based on expert knowledge or long-term accumulated experience. The inference engine can complete fuzzy reasoning according to the fuzzy control rules to obtain the fuzzy control quantity. The defuzzification interface is used to convert the obtained fuzzy control quantity into a clear control quantity output. The fuzzification interface fuzzifies the input quantity into a fuzzy vector, the inference engine performs fuzzy reasoning to solve the fuzzy relationship equation to obtain the fuzzy control quantity, and the defuzzification interface converts the fuzzy control quantity to obtain the adjustment coefficient K 1 and power distribution coefficient K 2 The control output value is used as the output of the fuzzy control module; the adjustment coefficient K 1 and standard regulation power P sr The multiplication result is used as the adjustment power P r , standard adjustment power P sr The value is taken within 70% to 90% of the peak power of the engine, and finally the power P is adjusted r and the engine power P at the previous moment f Add together as the engine output power P engine_i . According to the engine output power P engine_i, combined with the engine minimum fuel consumption characteristic curve to determine the engine speed n i . The power distribution coefficient K 2 With engine output power P engine_i Multiply the power P output by the engine to the power battery b .

[0049] Second, see Figure 7 During the braking process of the vehicle, set the power battery temperature threshold T th And the power battery SOC threshold SOC th The suitable working temperature of the power battery is between 0-40℃. If the power battery is overcharged, crystals will form inside the battery and break through the positive and negative electrode diaphragms, which may cause the power battery to short-circuit and catch fire. Therefore, the power battery temperature threshold T th Set to 40℃, power battery SOC threshold SOC th Set to 95%. When one of the following two situations occurs, mechanical braking is used for braking. The first situation: battery temperature T i Greater than or equal to the temperature threshold T th , the second case: battery SOC value SOC i Greater than or equal to SOC threshold SOC th When the battery temperature T i Less than the temperature threshold T th And the battery SOC value SOC i Less than SOC threshold SOC th When braking, the braking energy recovery method is used.

[0050] The specific steps include:

[0051] Step 1, determine the input quantity of the fuzzy control module;

[0052] The power battery SOC value measured by the battery monitoring system on the series hybrid vehicle is recorded as SOC i , the battery temperature at this time is measured by the temperature sensor and recorded as T i .

[0053] The vehicle curb weight, road slope, rolling resistance coefficient, air resistance coefficient, and driving speed parameters of the series hybrid vehicle are measured, and the calculation formula for the vehicle's required power is:

[0054]

[0055] P req is the vehicle power requirement, v is the vehicle speed, m is the vehicle curb weight, g is the acceleration of gravity, f is the rolling resistance coefficient, α is the road slope, C Dis the air resistance coefficient, A is the frontal area of ​​the vehicle, δ is the mass conversion coefficient, η t is the mechanical efficiency of the drive shaft.

[0056] Step 2, determine the basic domain and fuzzy subset;

[0057] The battery SOC value SOC i , battery temperature T i 、Vehicle power requirement P req As the input variable of the fuzzy control module, the adjustment coefficient K 1 and power distribution coefficient K 2 As the output variable of the fuzzy control module.

[0058] For input: the basic domain of battery SOC value is [5%, 95%], which is divided into 3 fuzzy subsets. The present invention selects low (L), medium (M), and high (H) as fuzzy language values, and each fuzzy language value corresponds to a fuzzy subset; the basic domain of battery temperature is [0℃, 40℃], which is divided into 3 fuzzy subsets. The present invention selects low (L), medium (M), and high (H) as fuzzy language values, and each fuzzy language value corresponds to a fuzzy subset; the engine peak power P of series hybrid electric vehicle max The present invention uses the common 110KW for illustration, so the basic domain of the vehicle's required power is [0, 110], which is divided into 5 fuzzy subsets. The present invention selects minimum (VS), small (S), medium (M), large (B), and maximum (VB) as fuzzy language values, and each fuzzy language value corresponds to a fuzzy subset.

[0059] For output: Regulation factor K 1 The basic domain is divided into 7 fuzzy subsets. The present invention selects negative big (NB), negative middle (NM), negative small (NL), zero (ZO), positive small (PL), positive middle (PM), and positive big (PB) as fuzzy language values. Each fuzzy language value corresponds to a fuzzy subset. The power allocation coefficient K 2 Its basic domain is divided into five fuzzy subsets. The present invention selects very small (VL), small (L), medium (M), large (B), and very large (VB) as fuzzy language values, and each fuzzy language value corresponds to a fuzzy subset.

[0060] Step 3, design fuzzy rules;

[0061] The operation process of the energy management method designed by the present invention is described as follows:

[0062] When the battery SOC value is small, the engine serves as the main power source. If the battery temperature is too high, most of the power generated by the engine is used to drive the entire vehicle. If the battery temperature is moderate or too low, part of the power generated by the engine is used to drive the entire vehicle, and the remaining power is used to charge the power battery.

[0063] When the battery SOC value is moderate, the engine and battery serve as power sources together. If the battery temperature is too high, the vehicle is mainly driven by the engine. If the battery temperature is moderate or too low, the vehicle power is shared by both.

[0064] When the battery SOC value is large and the battery temperature is moderate, the battery serves as the main power source and the engine assists in driving the entire vehicle.

[0065] Fuzzy control rules are formulated according to the above working process. The reasoning method adopts the Mamdani algorithm. A total of 90 fuzzy control rules are established using the IF-THEN rule. When the battery SOC value is L, the fuzzy control rules are shown in Table 1. When the battery SOC value is M, the fuzzy control rules are shown in Table 2. When the battery SOC value is H, the fuzzy control rules are shown in Table 3.

[0066] Table 1 Fuzzy rules when the battery SOC value is L

[0067]

[0068] Table 2 Fuzzy rules when the battery SOC value is M

[0069]

[0070] Table 3 Fuzzy rules when the battery SOC value is H

[0071]

[0072] In Table 1, Table 2 and Table 3, T corresponds to the battery temperature, P corresponds to the vehicle power requirement, and the fuzzy language values ​​of the battery temperature and the vehicle power requirement both correspond to two fuzzy language values, which are K 1 , K 2 The membership function diagram can be used to determine the membership of the battery SOC value, battery temperature, and vehicle power demand. For example, when the input battery temperature is 25°C, Figure 3 It can be obtained that at this time, the membership degree of the battery temperature is medium (M) is 0.8, and the membership degree of the battery temperature is high (H) is 0.2. The battery SOC value membership function diagram, battery temperature membership function diagram, vehicle demand power membership function diagram, adjustment coefficient membership function diagram and power allocation coefficient membership function diagram are shown in turn. Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 shown.

[0073] Step 4, fuzzy reasoning;

[0074] Using the Mamdani fuzzy inference method, the fuzzy subsets of battery temperature, battery SOC value, and vehicle power requirement are set A, set B, and set C respectively, and the adjustment coefficient K 1 , power allocation coefficient K 2 The sets formed by the fuzzy subsets of are set X and set Y respectively.

[0075] According to Mamdani fuzzy reasoning method, we have:

[0076] R(K 1 )=μ A (x)Λμ B (y)Λμ C (z)Λμ X (K 1 )

[0077] R(K 2 )=μ A (x)Λμ B (y)Λμ C (z)Λμ Y (K 2 )

[0078] Where: R(K 1 )、R(K 2 ) are the SOC input in fuzzy reasoning. i , T i , P req And the output K 1 , K 2 The fuzzy relationship between them, Λ is the smaller operation, μ A (x), μ B (y), μ C (z), μ X (K 1 ), μ Y (K 2 ) are the membership functions of battery temperature, battery SOC value, vehicle demand power, regulation coefficient and power allocation coefficient respectively.

[0079]

[0080]

[0081] Where: A * , B * , C * Indicates established facts, X *, Y * Indicates the result of reasoning; is A∩A * The maximum value of the membership function indicates that A * The fitness of A, ω B and ω C Meaning and ω A Similarly, they represent B * The suitability of B and C * The adaptability to C; ∨ is the larger operation.

[0082] For example, when the input battery temperature is 25°C, the battery SOC value is 0.8, and the vehicle demand power is 100KW, the membership of the battery temperature is medium (M) is 0.8, and the membership of the battery temperature is high (H) is 0.2; the membership of the battery SOC value is medium (M) is 0.3, and the membership of the battery SOC value is high (H) is 0.7; the membership of the demand power is large (B) is 0.2, and the membership of the vehicle demand power is maximum (VB) is 0.8.

[0083] When the battery temperature is medium (M), the battery SOC value is medium (M) and the vehicle power demand is large (B), according to Table 2, the adjustment coefficient K 1 is the middle (PM), the power distribution coefficient K 2 is small (L). The output of fuzzy reasoning corresponding to this situation is, Figure 8 As shown:

[0084]

[0085] Where: Indicates the adjustment coefficient K 1 The membership function when it is the middle (PM).

[0086] Similarly, we get the output of all fuzzy reasoning, and then get K 1 The total fuzzy inference output of is the union of all fuzzy inference outputs:

[0087]

[0088] Following the same process, we can get K 2 The total output μ of fuzzy reasoning Y* (K 2 ).

[0089] Step 5, defuzzification;

[0090] Select the centroid method to calculate the adjustment coefficient K 1 and power distribution coefficient K 2 The control output value is calculated as follows:

[0091]

[0092]

[0093] Where: x 1 is the value of the adjustment coefficient in the basic domain, x 2 is the value within the basic domain of the power allocation coefficient.

[0094] Step 6, determining the engine speed;

[0095] The adjustment factor K 1 and standard regulation power P sr Multiply as the regulating power P r , and finally adjust the power P r and the engine power P at the previous moment f Add together as the engine output power P engine_i , the engine power at the last moment P f is the power P output by the engine at the last moment engine_i-1 . According to the engine output power P engine_i , combined with the engine minimum fuel consumption characteristic curve to determine the engine speed n i , thereby ensuring the fuel economy of the car.

[0096] Specifically, the engine output power P engine_i The engine speed n is determined by the engine minimum fuel consumption characteristic curve i。 Engine output power P engine_i and engine speed n i is a one-to-one correspondence, for example, Fig. 9 The engine minimum fuel consumption characteristic curve corresponding to A1, A2 and A3 is shown in FIG. engine_i P 1 When the corresponding engine speed is n 1 .

[0097] Step 7, engine power distribution;

[0098] According to the power distribution coefficient K 2 The power distribution of the engine can be obtained. Specifically, the power distribution coefficient K 2 With engine output power P engine_i Multiply to get the power P output from the engine to the power battery b , that is, the power P that the engine charges the power battery through the generator b The remaining power is the power P generated by the engine for driving the vehicle. d , as shown in the following formula:

[0099]

[0100] Where: P b is the power output from the engine to the power battery, P d The power generated by the engine for driving the entire vehicle.

[0101] For example, the power allocation factor K 2 is 0.6 and the engine output power P engine_i When it is 80KW, the power P output by the engine to the power battery b The power P used for driving the vehicle is 48KW. d It is 32KW.

Claims

1. An energy management method for a series hybrid electric vehicle, It is characterized in that The hybrid electric vehicle driving process includes the following steps: S1, obtaining a regulation coefficient and a power distribution coefficient according to the vehicle power requirement, battery SOC value and battery temperature of the hybrid vehicle; S2, multiplying the adjustment coefficient and the standard adjustment power to obtain the adjustment power, the standard adjustment power is within 70% to 90% of the peak power of the hybrid vehicle engine, adding the adjustment power and the power of the hybrid vehicle engine at the previous moment to obtain the output power of the hybrid vehicle engine, and determining the engine speed of the hybrid vehicle according to the output power of the hybrid vehicle engine and the minimum fuel consumption characteristic curve of the hybrid vehicle engine; S3, the power distribution coefficient and the output power P of the hybrid vehicle engine are compared. engine_i The power P output by the hybrid vehicle engine to the power battery is obtained by multiplying b , P engine_i With P b The difference is the power generated by the engine of the hybrid vehicle for driving the entire vehicle; During the hybrid electric vehicle braking process, when the battery temperature is less than a temperature threshold and the battery SOC value is less than an SOC threshold, braking is performed using a braking energy recovery method.

2. The energy management method of a series hybrid electric vehicle according to claim 1, It is characterized in that The vehicle power requirement of the hybrid vehicle described in S1 is calculated according to the following formula: Where P req is the vehicle power requirement, v is the vehicle speed, m is the vehicle curb weight, g is the acceleration of gravity, f is the rolling resistance coefficient, α is the road slope, C D is the air resistance coefficient, A is the frontal area of ​​the vehicle, δ is the mass conversion coefficient, η t is the mechanical efficiency of the drive shaft.

3. The energy management method of a series hybrid electric vehicle according to claim 1, It is characterized in that S1 first fuzzifies the vehicle demand power, battery SOC value and battery temperature of the hybrid vehicle into fuzzy vectors, then performs fuzzy reasoning to solve the fuzzy control rules corresponding to the fuzzy reasoning to obtain the fuzzy control quantities of the adjustment coefficient and the power allocation coefficient, and finally defuzzifies the fuzzy control quantities of the adjustment coefficient and the power allocation coefficient by combining the center of gravity method to obtain the adjustment coefficient and the power allocation coefficient.

4. The energy management method of a series hybrid electric vehicle according to claim 3, It is characterized in that When the vehicle power requirement is fuzzy, the basic domain is [0, P max ], and the corresponding fuzzy language values ​​are extremely small, small, medium, large and extremely large; When the battery SOC value is fuzzified, the basic domain is [5%, 95%], and the corresponding fuzzy language values ​​are low, medium and high respectively; When the battery temperature is fuzzified, the basic domain is [0°C, 40°C], and the corresponding fuzzy language values ​​are low, medium and high respectively.

5. The energy management method of a series hybrid electric vehicle according to claim 4, It is characterized in that The fuzzy language values ​​corresponding to the basic domain of the adjustment coefficient are respectively negative large, negative medium, negative small, zero, positive small, positive medium and positive large, and the fuzzy language values ​​corresponding to the basic domain of the power allocation coefficient are respectively very small, small, medium, large and very large.

6. The energy management method of a series hybrid electric vehicle according to claim 5, It is characterized in that The fuzzy reasoning described in S1 is performed using the Mamdani algorithm, and then the IF-THEN rule is used to determine the vehicle demand power P req , Battery SOC value SOC i , battery temperature T i And the corresponding adjustment coefficient K 1 and power distribution coefficient K 2 90 fuzzy control rules are established in sequence.

7. The energy management method of a series hybrid electric vehicle according to claim 6, It is characterized in that In S1, Mamdani fuzzy reasoning method is used to obtain K 1 and K 2 The fuzzy control quantity is as follows: S11, let T i , SOC i , P req The sets formed by the fuzzy subsets are set A, set B, set C, K 1 , K 2 The sets formed by the fuzzy subsets are set X and set Y. According to Mamdani fuzzy reasoning method, we have: R(K 1 )=μ A (x)Lm B (y)Lm C (z)Lm X (K 1 ) R(K 2 )=μ A (x)Lm B (y)Lm C (z)Lm Y (K 2 ) Where: R(K 1 )、R(K 2 ) are the SOC input in fuzzy reasoning i , T i , P req And the output K 1 , K 2 The fuzzy relationship between them, the operation corresponding to Λ is to take the smaller one, μ A (x), μ B (y), μ C (z), μ X (K 1 ), μ Y (K 2 ) are T i , SOC i , P req , K 1 and K 2 The membership function of S12, according to R(K 1 ) to get K 1 The total output of fuzzy reasoning According to R(K 2 ) to get K 2 The total output of fuzzy reasoning S13, and Using the center of gravity method, the fuzzy control quantities of the adjustment coefficient and the power allocation coefficient are defuzzified to obtain K 1 and K 2 .

8. The energy management method of a series hybrid electric vehicle according to claim 7, It is characterized in that S13 K 1 and K 2 According to the following formulas: Where: x 1 is the value in the basic domain of the adjustment coefficient, x 2 is the value within the basic domain of the power allocation coefficient.

9. The energy management method of a series hybrid electric vehicle according to claim 1, It is characterized in that During the hybrid vehicle braking process, a mechanical braking method is used for braking when one of the following two situations occurs: the first situation is that the battery temperature is greater than or equal to the temperature threshold, and the second situation is that the battery SOC value is greater than or equal to the SOC threshold.

Images